McMahan et al. first proposed the idea of FL as a method to allow data to remain on distributed devices while training a shared model by aggregating locally trained updates [1]}. For a comprehensive overview of FL, refer to the technical report by Kairouz et al. [2]}.
There are two main challenges for FL methods. First is the need for communicating over unreliable networks to transmit data. A second issue results from data captured in varying contexts, leading to datasets that are statistically dissimilar, non-IID, between clients.
